The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here

Problem with the naives bayes learner

isuarezisuarez Member Posts: 3 Contributor I
edited November 2018 in Help
Hello i am having some problems with the Naives Bayes learner, it looks like when you add a X-Validation and set the parameters to full estimation mode with a fix kernel setting, the learner start doing non-predictions for most of the rows. I think it could be a bug, can anybody help me please.

This is my model:

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
  <context>
    <input>
      <location/>
    </input>
    <output>
      <location/>
      <location/>
    </output>
    <macros/>
  </context>
  <operator activated="true" class="process" expanded="true" name="Root">
    <process expanded="true" height="444" width="435">
      <operator activated="true" class="retrieve" expanded="true" height="60" name="Retrieve (2)" width="90" x="45" y="30">
        <parameter key="repository_entry" value="adult"/>
      </operator>
      <operator activated="true" class="sample" expanded="true" height="76" name="Sample" width="90" x="179" y="30">
        <parameter key="sample_size" value="3500"/>
        <parameter key="use_local_random_seed" value="true"/>
      </operator>
      <operator activated="true" class="x_validation" expanded="true" height="112" name="Validation" width="90" x="315" y="30">
        <process expanded="true" height="444" width="200">
          <operator activated="true" class="naive_bayes_kernel" expanded="true" height="76" name="Naive Bayes (Kernel)" width="90" x="38" y="61">
            <parameter key="estimation_mode" value="full"/>
            <parameter key="bandwidth_selection" value="fix"/>
          </operator>
          <connect from_port="training" to_op="Naive Bayes (Kernel)" to_port="training set"/>
          <connect from_op="Naive Bayes (Kernel)" from_port="model" to_port="model"/>
          <portSpacing port="source_training" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
          <portSpacing port="sink_through 1" spacing="0"/>
        </process>
        <process expanded="true" height="444" width="200">
          <operator activated="true" class="apply_model" expanded="true" height="76" name="Apply Model" width="90" x="45" y="120">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="performance" expanded="true" height="76" name="Performance" width="90" x="92" y="251"/>
          <connect from_port="model" to_op="Apply Model" to_port="model"/>
          <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
          <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
          <connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
          <portSpacing port="source_model" spacing="18"/>
          <portSpacing port="source_test set" spacing="18"/>
          <portSpacing port="source_through 1" spacing="0"/>
          <portSpacing port="sink_averagable 1" spacing="0"/>
          <portSpacing port="sink_averagable 2" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve (2)" from_port="output" to_op="Sample" to_port="example set input"/>
      <connect from_op="Sample" from_port="example set output" to_op="Validation" to_port="training"/>
      <connect from_op="Validation" from_port="averagable 1" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>



And the data is extracted from this website:  http://archive.ics.uci.edu/ml/datasets/Census+Income

Thanks. 

Answers

  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531 Unicorn
    Hi,
    yes there's an problem inside. We will resolve it as soon as possible.

    Greetings,
      Sebastian
Sign In or Register to comment.